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Abstract

Abstract—Recent high profile developments of autonomous
learning thermostats by companies such as Nest Labs and
Honeywell have brought to the fore the possibility of ever greater
numbers of intelligent devices permeating our homes and working
environments into the future. However, the specific learning
approaches and methodologies utilised by these devices have
never been made public. In fact little information is known as to
the specifics of how these devices operate and learn about their
environments or the users who use them. This paper proposes a
suitable learning architecture for such an intelligent thermostat
in the hope that it will benefit further investigation by the
research community. Our architecture comprises a number of
different learning methods each of which contributes to create a
complete autonomous thermostat capable of controlling a HVAC
system. A novel state action space formalism is proposed to
enable a Reinforcement Learning agent to successfully control the
HVAC system by optimising both occupant comfort and energy
costs. Our results show that the learning thermostat can achieve
cost savings of 10% over a programmable thermostat, whilst
maintaining high occupant comfort standards.

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